{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
      "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
      "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
      "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
      "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
      "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
      "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
      "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
      "       'symmetry_worst', 'fractal_dimension_worst'],\n",
      "      dtype='object')\n",
      "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
      "0    842302         M        17.99         10.38          122.80     1001.0   \n",
      "1    842517         M        20.57         17.77          132.90     1326.0   \n",
      "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
      "3  84348301         M        11.42         20.38           77.58      386.1   \n",
      "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
      "\n",
      "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
      "0          0.11840           0.27760          0.3001              0.14710   \n",
      "1          0.08474           0.07864          0.0869              0.07017   \n",
      "2          0.10960           0.15990          0.1974              0.12790   \n",
      "3          0.14250           0.28390          0.2414              0.10520   \n",
      "4          0.10030           0.13280          0.1980              0.10430   \n",
      "\n",
      "   symmetry_mean  fractal_dimension_mean  radius_se  texture_se  perimeter_se  \\\n",
      "0         0.2419                 0.07871     1.0950      0.9053         8.589   \n",
      "1         0.1812                 0.05667     0.5435      0.7339         3.398   \n",
      "2         0.2069                 0.05999     0.7456      0.7869         4.585   \n",
      "3         0.2597                 0.09744     0.4956      1.1560         3.445   \n",
      "4         0.1809                 0.05883     0.7572      0.7813         5.438   \n",
      "\n",
      "   area_se  smoothness_se  compactness_se  concavity_se  concave points_se  \\\n",
      "0   153.40       0.006399         0.04904       0.05373            0.01587   \n",
      "1    74.08       0.005225         0.01308       0.01860            0.01340   \n",
      "2    94.03       0.006150         0.04006       0.03832            0.02058   \n",
      "3    27.23       0.009110         0.07458       0.05661            0.01867   \n",
      "4    94.44       0.011490         0.02461       0.05688            0.01885   \n",
      "\n",
      "   symmetry_se  fractal_dimension_se  radius_worst  texture_worst  \\\n",
      "0      0.03003              0.006193         25.38          17.33   \n",
      "1      0.01389              0.003532         24.99          23.41   \n",
      "2      0.02250              0.004571         23.57          25.53   \n",
      "3      0.05963              0.009208         14.91          26.50   \n",
      "4      0.01756              0.005115         22.54          16.67   \n",
      "\n",
      "   perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
      "0           184.60      2019.0            0.1622             0.6656   \n",
      "1           158.80      1956.0            0.1238             0.1866   \n",
      "2           152.50      1709.0            0.1444             0.4245   \n",
      "3            98.87       567.7            0.2098             0.8663   \n",
      "4           152.20      1575.0            0.1374             0.2050   \n",
      "\n",
      "   concavity_worst  concave points_worst  symmetry_worst  \\\n",
      "0           0.7119                0.2654          0.4601   \n",
      "1           0.2416                0.1860          0.2750   \n",
      "2           0.4504                0.2430          0.3613   \n",
      "3           0.6869                0.2575          0.6638   \n",
      "4           0.4000                0.1625          0.2364   \n",
      "\n",
      "   fractal_dimension_worst  \n",
      "0                  0.11890  \n",
      "1                  0.08902  \n",
      "2                  0.08758  \n",
      "3                  0.17300  \n",
      "4                  0.07678  \n",
      "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
      "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
      "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
      "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
      "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
      "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
      "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
      "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
      "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
      "\n",
      "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
      "count       569.000000        569.000000      569.000000           569.000000   \n",
      "mean          0.096360          0.104341        0.088799             0.048919   \n",
      "std           0.014064          0.052813        0.079720             0.038803   \n",
      "min           0.052630          0.019380        0.000000             0.000000   \n",
      "25%           0.086370          0.064920        0.029560             0.020310   \n",
      "50%           0.095870          0.092630        0.061540             0.033500   \n",
      "75%           0.105300          0.130400        0.130700             0.074000   \n",
      "max           0.163400          0.345400        0.426800             0.201200   \n",
      "\n",
      "       symmetry_mean  fractal_dimension_mean   radius_se  texture_se  \\\n",
      "count     569.000000              569.000000  569.000000  569.000000   \n",
      "mean        0.181162                0.062798    0.405172    1.216853   \n",
      "std         0.027414                0.007060    0.277313    0.551648   \n",
      "min         0.106000                0.049960    0.111500    0.360200   \n",
      "25%         0.161900                0.057700    0.232400    0.833900   \n",
      "50%         0.179200                0.061540    0.324200    1.108000   \n",
      "75%         0.195700                0.066120    0.478900    1.474000   \n",
      "max         0.304000                0.097440    2.873000    4.885000   \n",
      "\n",
      "       perimeter_se     area_se  smoothness_se  compactness_se  concavity_se  \\\n",
      "count    569.000000  569.000000     569.000000      569.000000    569.000000   \n",
      "mean       2.866059   40.337079       0.007041        0.025478      0.031894   \n",
      "std        2.021855   45.491006       0.003003        0.017908      0.030186   \n",
      "min        0.757000    6.802000       0.001713        0.002252      0.000000   \n",
      "25%        1.606000   17.850000       0.005169        0.013080      0.015090   \n",
      "50%        2.287000   24.530000       0.006380        0.020450      0.025890   \n",
      "75%        3.357000   45.190000       0.008146        0.032450      0.042050   \n",
      "max       21.980000  542.200000       0.031130        0.135400      0.396000   \n",
      "\n",
      "       concave points_se  symmetry_se  fractal_dimension_se  radius_worst  \\\n",
      "count         569.000000   569.000000            569.000000    569.000000   \n",
      "mean            0.011796     0.020542              0.003795     16.269190   \n",
      "std             0.006170     0.008266              0.002646      4.833242   \n",
      "min             0.000000     0.007882              0.000895      7.930000   \n",
      "25%             0.007638     0.015160              0.002248     13.010000   \n",
      "50%             0.010930     0.018730              0.003187     14.970000   \n",
      "75%             0.014710     0.023480              0.004558     18.790000   \n",
      "max             0.052790     0.078950              0.029840     36.040000   \n",
      "\n",
      "       texture_worst  perimeter_worst   area_worst  smoothness_worst  \\\n",
      "count     569.000000       569.000000   569.000000        569.000000   \n",
      "mean       25.677223       107.261213   880.583128          0.132369   \n",
      "std         6.146258        33.602542   569.356993          0.022832   \n",
      "min        12.020000        50.410000   185.200000          0.071170   \n",
      "25%        21.080000        84.110000   515.300000          0.116600   \n",
      "50%        25.410000        97.660000   686.500000          0.131300   \n",
      "75%        29.720000       125.400000  1084.000000          0.146000   \n",
      "max        49.540000       251.200000  4254.000000          0.222600   \n",
      "\n",
      "       compactness_worst  concavity_worst  concave points_worst  \\\n",
      "count         569.000000       569.000000            569.000000   \n",
      "mean            0.254265         0.272188              0.114606   \n",
      "std             0.157336         0.208624              0.065732   \n",
      "min             0.027290         0.000000              0.000000   \n",
      "25%             0.147200         0.114500              0.064930   \n",
      "50%             0.211900         0.226700              0.099930   \n",
      "75%             0.339100         0.382900              0.161400   \n",
      "max             1.058000         1.252000              0.291000   \n",
      "\n",
      "       symmetry_worst  fractal_dimension_worst  \n",
      "count      569.000000               569.000000  \n",
      "mean         0.290076                 0.083946  \n",
      "std          0.061867                 0.018061  \n",
      "min          0.156500                 0.055040  \n",
      "25%          0.250400                 0.071460  \n",
      "50%          0.282200                 0.080040  \n",
      "75%          0.317900                 0.092080  \n",
      "max          0.663800                 0.207500  \n"
     ]
    }
   ],
   "source": [
    "# 导入并观察数据\n",
    "data = pd.read_csv('data.csv')\n",
    "pd.set_option('display.max_columns', None)\n",
    "print(data.columns)\n",
    "print(data.head())\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将特征字段分为三组\n",
    "features_mean = list(data.columns[2:12])\n",
    "features_se = list(data.columns[12:22])\n",
    "deatures_worst = list(data.columns[22:32])\n",
    "# 数据清洗\n",
    "# 删除id列\n",
    "data.drop('id', axis=1, inplace=True)\n",
    "# 将标签B良性替换为0，M恶性替换为1\n",
    "data['diagnosis'] = data['diagnosis'].map({'B':0, 'M':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1105b07f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ce59908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125557f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将肿瘤诊断结果可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(data['diagnosis'], label='Count')\n",
    "# 热力图呈现feature_mean字段之间相关性\n",
    "corr = data[features_mean].corr()\n",
    "plt.figure(figsize=(14, 14))\n",
    "sns.heatmap(corr, annot=True) # annot显示方格中数字"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>特征选择：  \n",
    "1、mean，se，worst是对同一组内容的不同度量方式，保留mean  \n",
    "2、radius_mean，perimeter_mean，area_mean三个相关属性大，保留radius_mean  \n",
    "3、compactness_mean，daconcavity_mean，concave points_mean三个相关属性大，保留compactness_mean</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征选择\n",
    "features_remain = ['radius_mean','texture_mean', 'smoothness_mean','compactness_mean','symmetry_mean', 'fractal_dimension_mean'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抽取30%数据作为测试集，其余作为训练集\n",
    "train, test = train_test_split(data, test_size=0.3)\n",
    "train_X = train[features_remain]\n",
    "train_y = train['diagnosis']\n",
    "test_X = test[features_remain]\n",
    "test_y = test['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "train_X = StandardScaler().fit_transform(train_X)\n",
    "test_X = StandardScaler().fit_transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率为： 0.9122807017543859\n"
     ]
    }
   ],
   "source": [
    "# 训练svm并测试\n",
    "model = svm.SVC()\n",
    "model.fit(train_X, train_y)\n",
    "prediction = model.predict(test_X)\n",
    "# 计算准确率\n",
    "print('准确率为：', metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
